Overview

Dataset statistics

Number of variables20
Number of observations45376
Missing cells67144
Missing cells (%)7.4%
Duplicate rows16
Duplicate rows (%)< 0.1%
Total size in memory6.9 MiB
Average record size in memory160.0 B

Variable types

Text9
Numeric9
DateTime1
Categorical1

Alerts

Dataset has 16 (< 0.1%) duplicate rowsDuplicates
status is highly imbalanced (97.0%)Imbalance
collection_name has 40888 (90.1%) missing valuesMissing
overview has 941 (2.1%) missing valuesMissing
tagline has 24978 (55.0%) missing valuesMissing
popularity is highly skewed (γ1 = 29.21506573)Skewed
return is highly skewed (γ1 = 138.3295261)Skewed
budget has 36490 (80.4%) zerosZeros
revenue has 37969 (83.7%) zerosZeros
runtime has 1535 (3.4%) zerosZeros
vote_average has 2947 (6.5%) zerosZeros
vote_count has 2849 (6.3%) zerosZeros
return has 39995 (88.1%) zerosZeros

Reproduction

Analysis started2024-07-04 23:47:27.934008
Analysis finished2024-07-04 23:47:42.141342
Duration14.21 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

collection_name
Text

MISSING 

Distinct1695
Distinct (%)37.8%
Missing40888
Missing (%)90.1%
Memory size354.6 KiB
2024-07-04T18:47:42.392943image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length54
Median length43
Mean length23.855838
Min length3

Characters and Unicode

Total characters107065
Distinct characters166
Distinct categories12 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)8.7%

Sample

1st rowToy Story Collection
2nd rowGrumpy Old Men Collection
3rd rowFather of the Bride Collection
4th rowJames Bond Collection
5th rowBalto Collection
ValueCountFrequency (%)
collection 3743
25.3%
the 1146
 
7.8%
of 230
 
1.6%
series 147
 
1.0%
139
 
0.9%
trilogy 87
 
0.6%
and 84
 
0.6%
man 62
 
0.4%
a 62
 
0.4%
in 56
 
0.4%
Other values (2407) 9028
61.1%
2024-07-04T18:47:42.792885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 11114
 
10.4%
e 10450
 
9.8%
10297
 
9.6%
l 10200
 
9.5%
i 7559
 
7.1%
n 7403
 
6.9%
t 6488
 
6.1%
c 4845
 
4.5%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (156) 29776
27.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 81103
75.8%
Uppercase Letter 13885
 
13.0%
Space Separator 10297
 
9.6%
Other Punctuation 576
 
0.5%
Open Punctuation 335
 
0.3%
Close Punctuation 335
 
0.3%
Decimal Number 321
 
0.3%
Dash Punctuation 162
 
0.2%
Other Letter 37
 
< 0.1%
Final Punctuation 9
 
< 0.1%
Other values (2) 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 11114
13.7%
e 10450
12.9%
l 10200
12.6%
i 7559
9.3%
n 7403
9.1%
t 6488
8.0%
c 4845
 
6.0%
a 4459
 
5.5%
r 3870
 
4.8%
s 2588
 
3.2%
Other values (69) 12127
15.0%
Uppercase Letter
ValueCountFrequency (%)
C 4474
32.2%
T 1527
 
11.0%
S 1063
 
7.7%
B 682
 
4.9%
M 630
 
4.5%
A 509
 
3.7%
D 505
 
3.6%
H 462
 
3.3%
P 432
 
3.1%
G 417
 
3.0%
Other values (33) 3184
22.9%
Other Letter
ValueCountFrequency (%)
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
3
 
8.1%
2
 
5.4%
Other values (4) 8
21.6%
Other Punctuation
ValueCountFrequency (%)
. 172
29.9%
' 107
18.6%
: 99
17.2%
, 79
13.7%
& 52
 
9.0%
! 35
 
6.1%
/ 21
 
3.6%
? 4
 
0.7%
* 4
 
0.7%
3
 
0.5%
Decimal Number
ValueCountFrequency (%)
1 80
24.9%
9 64
19.9%
3 54
16.8%
0 51
15.9%
2 21
 
6.5%
8 13
 
4.0%
5 12
 
3.7%
7 11
 
3.4%
6 10
 
3.1%
4 5
 
1.6%
Open Punctuation
ValueCountFrequency (%)
( 330
98.5%
[ 5
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 330
98.5%
] 5
 
1.5%
Dash Punctuation
ValueCountFrequency (%)
- 160
98.8%
2
 
1.2%
Space Separator
ValueCountFrequency (%)
10297
100.0%
Final Punctuation
ValueCountFrequency (%)
9
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%
Other Number
ValueCountFrequency (%)
½ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 94574
88.3%
Common 12040
 
11.2%
Cyrillic 414
 
0.4%
Hiragana 15
 
< 0.1%
Hangul 10
 
< 0.1%
Katakana 9
 
< 0.1%
Han 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 11114
11.8%
e 10450
11.0%
l 10200
10.8%
i 7559
 
8.0%
n 7403
 
7.8%
t 6488
 
6.9%
c 4845
 
5.1%
C 4474
 
4.7%
a 4459
 
4.7%
r 3870
 
4.1%
Other values (70) 23712
25.1%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
Common
ValueCountFrequency (%)
10297
85.5%
( 330
 
2.7%
) 330
 
2.7%
. 172
 
1.4%
- 160
 
1.3%
' 107
 
0.9%
: 99
 
0.8%
1 80
 
0.7%
, 79
 
0.7%
9 64
 
0.5%
Other values (20) 322
 
2.7%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%
Katakana
ValueCountFrequency (%)
3
33.3%
3
33.3%
3
33.3%
Han
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 106351
99.3%
Cyrillic 414
 
0.4%
None 246
 
0.2%
Hiragana 15
 
< 0.1%
Punctuation 14
 
< 0.1%
Katakana 12
 
< 0.1%
Hangul 10
 
< 0.1%
CJK 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 11114
 
10.5%
e 10450
 
9.8%
10297
 
9.7%
l 10200
 
9.6%
i 7559
 
7.1%
n 7403
 
7.0%
t 6488
 
6.1%
c 4845
 
4.6%
C 4474
 
4.2%
a 4459
 
4.2%
Other values (67) 29062
27.3%
Cyrillic
ValueCountFrequency (%)
л 48
 
11.6%
и 41
 
9.9%
о 37
 
8.9%
к 30
 
7.2%
е 27
 
6.5%
я 25
 
6.0%
а 17
 
4.1%
ц 16
 
3.9%
К 16
 
3.9%
р 14
 
3.4%
Other values (32) 143
34.5%
None
ValueCountFrequency (%)
é 45
18.3%
ä 40
16.3%
ô 35
14.2%
ò 28
11.4%
ö 19
7.7%
ó 14
 
5.7%
ı 14
 
5.7%
í 9
 
3.7%
á 4
 
1.6%
İ 4
 
1.6%
Other values (19) 34
13.8%
Punctuation
ValueCountFrequency (%)
9
64.3%
3
 
21.4%
2
 
14.3%
Hiragana
ValueCountFrequency (%)
3
20.0%
3
20.0%
3
20.0%
3
20.0%
3
20.0%
Katakana
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%
CJK
ValueCountFrequency (%)
3
100.0%
Hangul
ValueCountFrequency (%)
2
20.0%
2
20.0%
2
20.0%
2
20.0%
2
20.0%

budget
Real number (ℝ)

ZEROS 

Distinct1223
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4232604.4
Minimum0
Maximum3.8 × 108
Zeros36490
Zeros (%)80.4%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:42.912843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile25000000
Maximum3.8 × 108
Range3.8 × 108
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17439860
Coefficient of variation (CV)4.1203614
Kurtosis66.634491
Mean4232604.4
Median Absolute Deviation (MAD)0
Skewness7.1183385
Sum1.9205866 × 1011
Variance3.041487 × 1014
MonotonicityNot monotonic
2024-07-04T18:47:43.050869image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 36490
80.4%
5000000 286
 
0.6%
10000000 259
 
0.6%
20000000 243
 
0.5%
2000000 242
 
0.5%
15000000 226
 
0.5%
3000000 223
 
0.5%
25000000 206
 
0.5%
1000000 197
 
0.4%
30000000 190
 
0.4%
Other values (1213) 6814
 
15.0%
ValueCountFrequency (%)
0 36490
80.4%
1 25
 
0.1%
2 14
 
< 0.1%
3 9
 
< 0.1%
4 8
 
< 0.1%
5 8
 
< 0.1%
6 5
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
380000000 1
 
< 0.1%
300000000 1
 
< 0.1%
280000000 1
 
< 0.1%
270000000 1
 
< 0.1%
260000000 3
 
< 0.1%
258000000 1
 
< 0.1%
255000000 1
 
< 0.1%
250000000 10
< 0.1%
245000000 2
 
< 0.1%
237000000 1
 
< 0.1%

genres
Text

Distinct4065
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:43.179503image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length98
Median length80
Mean length21.610587
Min length2

Characters and Unicode

Total characters980602
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2364 ?
Unique (%)5.2%

Sample

1st row['Animation', 'Comedy', 'Family']
2nd row['Adventure', 'Fantasy', 'Family']
3rd row['Romance', 'Comedy']
4th row['Comedy', 'Drama', 'Romance']
5th row['Comedy']
ValueCountFrequency (%)
drama 20255
20.8%
comedy 13181
13.6%
thriller 7619
 
7.8%
romance 6733
 
6.9%
action 6592
 
6.8%
horror 4670
 
4.8%
crime 4305
 
4.4%
documentary 3921
 
4.0%
adventure 3494
 
3.6%
science 3042
 
3.1%
Other values (13) 23416
24.1%
2024-07-04T18:47:43.446596image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 182072
18.6%
r 69070
 
7.0%
a 61813
 
6.3%
e 55766
 
5.7%
m 53095
 
5.4%
51852
 
5.3%
o 48525
 
4.9%
, 48044
 
4.9%
[ 45376
 
4.6%
] 45376
 
4.6%
Other values (23) 319613
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 512272
52.2%
Other Punctuation 230116
23.5%
Uppercase Letter 95610
 
9.8%
Space Separator 51852
 
5.3%
Open Punctuation 45376
 
4.6%
Close Punctuation 45376
 
4.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 69070
13.5%
a 61813
12.1%
e 55766
10.9%
m 53095
10.4%
o 48525
9.5%
i 39656
7.7%
n 35664
7.0%
y 28508
5.6%
c 27970
5.5%
t 26197
 
5.1%
Other values (7) 66008
12.9%
Uppercase Letter
ValueCountFrequency (%)
D 24176
25.3%
C 17486
18.3%
A 12018
12.6%
F 9744
10.2%
T 8385
 
8.8%
R 6733
 
7.0%
H 6067
 
6.3%
M 4828
 
5.0%
S 3042
 
3.2%
W 2365
 
2.5%
Other Punctuation
ValueCountFrequency (%)
' 182072
79.1%
, 48044
 
20.9%
Space Separator
ValueCountFrequency (%)
51852
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 45376
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 607882
62.0%
Common 372720
38.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 69070
11.4%
a 61813
 
10.2%
e 55766
 
9.2%
m 53095
 
8.7%
o 48525
 
8.0%
i 39656
 
6.5%
n 35664
 
5.9%
y 28508
 
4.7%
c 27970
 
4.6%
t 26197
 
4.3%
Other values (18) 161618
26.6%
Common
ValueCountFrequency (%)
' 182072
48.8%
51852
 
13.9%
, 48044
 
12.9%
[ 45376
 
12.2%
] 45376
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 980602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 182072
18.6%
r 69070
 
7.0%
a 61813
 
6.3%
e 55766
 
5.7%
m 53095
 
5.4%
51852
 
5.3%
o 48525
 
4.9%
, 48044
 
4.9%
[ 45376
 
4.6%
] 45376
 
4.6%
Other values (23) 319613
32.6%

id
Real number (ℝ)

Distinct45346
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108027.1
Minimum2
Maximum469172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:43.568581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5348.75
Q126385.75
median59857.5
Q3156533.5
95-th percentile357194.5
Maximum469172
Range469170
Interquartile range (IQR)130147.75

Descriptive statistics

Standard deviation112168.38
Coefficient of variation (CV)1.0383355
Kurtosis0.55951556
Mean108027.1
Median Absolute Deviation (MAD)44418.5
Skewness1.2830689
Sum4.9018378 × 109
Variance1.2581745 × 1010
MonotonicityNot monotonic
2024-07-04T18:47:43.673317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
141971 3
 
< 0.1%
97995 2
 
< 0.1%
10991 2
 
< 0.1%
109962 2
 
< 0.1%
119916 2
 
< 0.1%
159849 2
 
< 0.1%
84198 2
 
< 0.1%
132641 2
 
< 0.1%
168538 2
 
< 0.1%
99080 2
 
< 0.1%
Other values (45336) 45355
> 99.9%
ValueCountFrequency (%)
2 1
< 0.1%
3 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
14 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
ValueCountFrequency (%)
469172 1
< 0.1%
468707 1
< 0.1%
468343 1
< 0.1%
467731 1
< 0.1%
465044 1
< 0.1%
464819 1
< 0.1%
464207 1
< 0.1%
464111 1
< 0.1%
463906 1
< 0.1%
463800 1
< 0.1%
Distinct89
Distinct (%)0.2%
Missing11
Missing (%)< 0.1%
Memory size354.6 KiB
2024-07-04T18:47:43.781771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters90730
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)< 0.1%

Sample

1st rowen
2nd rowen
3rd rowen
4th rowen
5th rowen
ValueCountFrequency (%)
en 32202
71.0%
fr 2437
 
5.4%
it 1528
 
3.4%
ja 1349
 
3.0%
de 1078
 
2.4%
es 992
 
2.2%
ru 822
 
1.8%
hi 508
 
1.1%
ko 444
 
1.0%
zh 408
 
0.9%
Other values (79) 3597
 
7.9%
2024-07-04T18:47:43.983893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 34527
38.1%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (16) 6026
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90730
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 34527
38.1%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (16) 6026
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 90730
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 34527
38.1%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (16) 6026
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 34527
38.1%
n 32910
36.3%
r 3630
 
4.0%
f 2835
 
3.1%
i 2388
 
2.6%
t 2250
 
2.5%
a 1839
 
2.0%
s 1652
 
1.8%
j 1350
 
1.5%
d 1323
 
1.5%
Other values (16) 6026
 
6.6%

overview
Text

MISSING 

Distinct44232
Distinct (%)99.5%
Missing941
Missing (%)2.1%
Memory size354.6 KiB
2024-07-04T18:47:44.260166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length1000
Median length786
Mean length323.29706
Min length1

Characters and Unicode

Total characters14365705
Distinct characters429
Distinct categories25 ?
Distinct scripts13 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44173 ?
Unique (%)99.4%

Sample

1st rowLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.
2nd rowWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.
3rd rowA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.
4th rowCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.
5th rowJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.
ValueCountFrequency (%)
the 138082
 
5.6%
a 98889
 
4.0%
and 75259
 
3.1%
to 73321
 
3.0%
of 69574
 
2.8%
in 48143
 
2.0%
is 36500
 
1.5%
his 36165
 
1.5%
with 23902
 
1.0%
her 21484
 
0.9%
Other values (97091) 1827389
74.6%
2024-07-04T18:47:44.687191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2406350
16.8%
e 1363787
 
9.5%
a 940502
 
6.5%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767851
 
5.3%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (419) 4103377
28.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 11150061
77.6%
Space Separator 2406388
 
16.8%
Uppercase Letter 390962
 
2.7%
Other Punctuation 312824
 
2.2%
Decimal Number 42223
 
0.3%
Dash Punctuation 36767
 
0.3%
Close Punctuation 10100
 
0.1%
Open Punctuation 10077
 
0.1%
Final Punctuation 4556
 
< 0.1%
Initial Punctuation 882
 
< 0.1%
Other values (15) 865
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1363787
12.2%
a 940502
 
8.4%
t 934766
 
8.4%
i 851514
 
7.6%
o 829873
 
7.4%
n 822601
 
7.4%
s 767851
 
6.9%
r 744274
 
6.7%
h 600810
 
5.4%
l 478813
 
4.3%
Other values (142) 2815270
25.2%
Uppercase Letter
ValueCountFrequency (%)
A 42751
 
10.9%
T 35968
 
9.2%
S 31126
 
8.0%
M 23954
 
6.1%
B 23699
 
6.1%
C 22803
 
5.8%
H 19429
 
5.0%
W 18652
 
4.8%
I 16798
 
4.3%
D 16311
 
4.2%
Other values (77) 139471
35.7%
Other Letter
ValueCountFrequency (%)
6
 
4.8%
6
 
4.8%
5
 
4.0%
4
 
3.2%
3
 
2.4%
3
 
2.4%
3
 
2.4%
3
 
2.4%
2
 
1.6%
م 2
 
1.6%
Other values (76) 88
70.4%
Other Punctuation
ValueCountFrequency (%)
, 133443
42.7%
. 124794
39.9%
' 31121
 
9.9%
" 11661
 
3.7%
: 3299
 
1.1%
? 2759
 
0.9%
; 2493
 
0.8%
! 1543
 
0.5%
/ 765
 
0.2%
& 453
 
0.1%
Other values (12) 493
 
0.2%
Nonspacing Mark
ValueCountFrequency (%)
́ 4
12.1%
ి 4
12.1%
3
9.1%
3
9.1%
3
9.1%
̈ 3
9.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
2
 
6.1%
Other values (4) 5
15.2%
Decimal Number
ValueCountFrequency (%)
1 9748
23.1%
0 8265
19.6%
9 6405
15.2%
2 4251
10.1%
5 2440
 
5.8%
8 2379
 
5.6%
3 2342
 
5.5%
4 2176
 
5.2%
7 2131
 
5.0%
6 2086
 
4.9%
Spacing Mark
ValueCountFrequency (%)
11
40.7%
4
 
14.8%
3
 
11.1%
3
 
11.1%
ि 2
 
7.4%
2
 
7.4%
1
 
3.7%
ி 1
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 35244
95.9%
881
 
2.4%
633
 
1.7%
5
 
< 0.1%
4
 
< 0.1%
Other Symbol
ValueCountFrequency (%)
® 45
70.3%
14
 
21.9%
¦ 2
 
3.1%
° 2
 
3.1%
1
 
1.6%
Math Symbol
ValueCountFrequency (%)
~ 20
50.0%
+ 11
27.5%
= 6
 
15.0%
| 2
 
5.0%
1
 
2.5%
Open Punctuation
ValueCountFrequency (%)
( 10024
99.5%
[ 50
 
0.5%
{ 2
 
< 0.1%
1
 
< 0.1%
Currency Symbol
ValueCountFrequency (%)
$ 317
96.4%
£ 10
 
3.0%
1
 
0.3%
1
 
0.3%
Space Separator
ValueCountFrequency (%)
2406350
> 99.9%
  36
 
< 0.1%
  2
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 10048
99.5%
] 50
 
0.5%
} 2
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
3847
84.4%
690
 
15.1%
» 19
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
672
76.2%
192
 
21.8%
« 18
 
2.0%
Control
ValueCountFrequency (%)
106
96.4%
’ 3
 
2.7%
 1
 
0.9%
Modifier Symbol
ValueCountFrequency (%)
´ 25
65.8%
` 12
31.6%
¯ 1
 
2.6%
Format
ValueCountFrequency (%)
31
60.8%
­ 20
39.2%
Other Number
ValueCountFrequency (%)
½ 8
50.0%
¹ 8
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 19
100.0%
Line Separator
ValueCountFrequency (%)
7
100.0%
Letter Number
ValueCountFrequency (%)
2
100.0%
Paragraph Separator
ValueCountFrequency (%)
2
100.0%
Modifier Letter
ValueCountFrequency (%)
ʼ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11535791
80.3%
Common 2824495
 
19.7%
Cyrillic 4587
 
< 0.1%
Greek 648
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Han 10
 
< 0.1%
Hangul 9
 
< 0.1%
Other values (3) 19
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1363787
11.8%
a 940502
 
8.2%
t 934766
 
8.1%
i 851514
 
7.4%
o 829873
 
7.2%
n 822601
 
7.1%
s 767851
 
6.7%
r 744274
 
6.5%
h 600810
 
5.2%
l 478813
 
4.2%
Other values (132) 3201000
27.7%
Common
ValueCountFrequency (%)
2406350
85.2%
, 133443
 
4.7%
. 124794
 
4.4%
- 35244
 
1.2%
' 31121
 
1.1%
" 11661
 
0.4%
) 10048
 
0.4%
( 10024
 
0.4%
1 9748
 
0.3%
0 8265
 
0.3%
Other values (71) 43797
 
1.6%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Greek
ValueCountFrequency (%)
α 60
 
9.3%
ο 55
 
8.5%
τ 43
 
6.6%
ι 36
 
5.6%
η 36
 
5.6%
ν 34
 
5.2%
ε 31
 
4.8%
ρ 31
 
4.8%
π 30
 
4.6%
ς 30
 
4.6%
Other values (33) 262
40.4%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Han
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Inherited
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14347707
99.9%
Punctuation 7270
 
0.1%
None 5930
 
< 0.1%
Cyrillic 4587
 
< 0.1%
Devanagari 77
 
< 0.1%
Telugu 30
 
< 0.1%
Hiragana 20
 
< 0.1%
Tamil 19
 
< 0.1%
Letterlike Symbols 14
 
< 0.1%
CJK 10
 
< 0.1%
Other values (11) 41
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2406350
16.8%
e 1363787
 
9.5%
a 940502
 
6.6%
t 934766
 
6.5%
i 851514
 
5.9%
o 829873
 
5.8%
n 822601
 
5.7%
s 767851
 
5.4%
r 744274
 
5.2%
h 600810
 
4.2%
Other values (82) 4085379
28.5%
Punctuation
ValueCountFrequency (%)
3847
52.9%
881
 
12.1%
690
 
9.5%
672
 
9.2%
633
 
8.7%
303
 
4.2%
192
 
2.6%
31
 
0.4%
7
 
0.1%
5
 
0.1%
Other values (4) 9
 
0.1%
None
ValueCountFrequency (%)
é 1552
26.2%
ä 294
 
5.0%
á 293
 
4.9%
ö 250
 
4.2%
í 243
 
4.1%
è 209
 
3.5%
ü 178
 
3.0%
ı 165
 
2.8%
ó 164
 
2.8%
ç 158
 
2.7%
Other values (141) 2424
40.9%
Cyrillic
ValueCountFrequency (%)
о 470
 
10.2%
е 404
 
8.8%
а 373
 
8.1%
н 323
 
7.0%
и 299
 
6.5%
т 265
 
5.8%
р 240
 
5.2%
с 218
 
4.8%
в 173
 
3.8%
л 161
 
3.5%
Other values (46) 1661
36.2%
Letterlike Symbols
ValueCountFrequency (%)
14
100.0%
Devanagari
ValueCountFrequency (%)
11
 
14.3%
6
 
7.8%
6
 
7.8%
5
 
6.5%
4
 
5.2%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
3
 
3.9%
Other values (21) 30
39.0%
Alphabetic PF
ValueCountFrequency (%)
4
100.0%
Hiragana
ValueCountFrequency (%)
4
20.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
1
 
5.0%
Other values (7) 7
35.0%
Diacriticals
ValueCountFrequency (%)
́ 4
57.1%
̈ 3
42.9%
Telugu
ValueCountFrequency (%)
ి 4
13.3%
3
10.0%
3
10.0%
3
10.0%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
2
 
6.7%
1
 
3.3%
Other values (6) 6
20.0%
Tamil
ValueCountFrequency (%)
3
15.8%
2
10.5%
2
10.5%
2
10.5%
2
10.5%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
1
 
5.3%
Other values (3) 3
15.8%
Arabic
ValueCountFrequency (%)
م 2
50.0%
ہ 1
25.0%
ت 1
25.0%
Hangul
ValueCountFrequency (%)
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Number Forms
ValueCountFrequency (%)
2
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ 2
100.0%
Thai
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
CJK
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Katakana
ValueCountFrequency (%)
1
100.0%
Currency Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Specials
ValueCountFrequency (%)
1
100.0%

popularity
Real number (ℝ)

SKEWED 

Distinct43731
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9264576
Minimum0
Maximum547.4883
Zeros40
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:44.795856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02079775
Q10.3888395
median1.1304545
Q33.6916945
95-th percentile11.063627
Maximum547.4883
Range547.4883
Interquartile range (IQR)3.302855

Descriptive statistics

Standard deviation6.0096718
Coefficient of variation (CV)2.0535653
Kurtosis1923.6882
Mean2.9264576
Median Absolute Deviation (MAD)0.9676215
Skewness29.215066
Sum132790.94
Variance36.116156
MonotonicityNot monotonic
2024-07-04T18:47:44.887055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-656
 
0.1%
0.000308 42
 
0.1%
0 40
 
0.1%
0.00022 39
 
0.1%
0.000844 38
 
0.1%
0.000578 38
 
0.1%
0.001177 38
 
0.1%
0.002001 27
 
0.1%
0.003013 21
 
< 0.1%
0.00353 19
 
< 0.1%
Other values (43721) 45018
99.2%
ValueCountFrequency (%)
0 40
0.1%
1 × 10-656
0.1%
2 × 10-66
 
< 0.1%
3 × 10-66
 
< 0.1%
4 × 10-65
 
< 0.1%
5 × 10-61
 
< 0.1%
6 × 10-62
 
< 0.1%
7 × 10-61
 
< 0.1%
8 × 10-66
 
< 0.1%
9 × 10-62
 
< 0.1%
ValueCountFrequency (%)
547.488298 1
< 0.1%
294.337037 1
< 0.1%
287.253654 1
< 0.1%
228.032744 1
< 0.1%
213.849907 1
< 0.1%
187.860492 1
< 0.1%
185.330992 1
< 0.1%
185.070892 1
< 0.1%
183.870374 1
< 0.1%
154.801009 1
< 0.1%
Distinct22668
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:45.136214image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length663
Median length520
Mean length35.816599
Min length2

Characters and Unicode

Total characters1625214
Distinct characters291
Distinct categories15 ?
Distinct scripts6 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20300 ?
Unique (%)44.7%

Sample

1st row['Pixar Animation Studios']
2nd row['TriStar Pictures', 'Teitler Film', 'Interscope Communications']
3rd row['Warner Bros.', 'Lancaster Gate']
4th row['Twentieth Century Fox Film Corporation']
5th row['Sandollar Productions', 'Touchstone Pictures']
ValueCountFrequency (%)
12640
 
6.7%
films 9455
 
5.0%
pictures 9267
 
4.9%
productions 9059
 
4.8%
film 6679
 
3.5%
entertainment 5154
 
2.7%
corporation 2189
 
1.2%
company 1769
 
0.9%
warner 1478
 
0.8%
bros 1411
 
0.7%
Other values (18621) 130372
68.8%
2024-07-04T18:47:45.514291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
144110
 
8.9%
' 140657
 
8.7%
i 106938
 
6.6%
e 94644
 
5.8%
n 89969
 
5.5%
o 85292
 
5.2%
r 83547
 
5.1%
t 83437
 
5.1%
a 77148
 
4.7%
s 62667
 
3.9%
Other values (281) 656805
40.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 987034
60.7%
Uppercase Letter 198965
 
12.2%
Other Punctuation 186170
 
11.5%
Space Separator 144110
 
8.9%
Open Punctuation 49704
 
3.1%
Close Punctuation 49703
 
3.1%
Decimal Number 4355
 
0.3%
Dash Punctuation 4331
 
0.3%
Math Symbol 662
 
< 0.1%
Other Letter 140
 
< 0.1%
Other values (5) 40
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 106938
10.8%
e 94644
9.6%
n 89969
9.1%
o 85292
8.6%
r 83547
8.5%
t 83437
8.5%
a 77148
 
7.8%
s 62667
 
6.3%
l 51264
 
5.2%
m 44275
 
4.5%
Other values (102) 207853
21.1%
Other Letter
ValueCountFrequency (%)
9
 
6.4%
8
 
5.7%
6
 
4.3%
5
 
3.6%
5
 
3.6%
5
 
3.6%
5
 
3.6%
5
 
3.6%
4
 
2.9%
3
 
2.1%
Other values (62) 85
60.7%
Uppercase Letter
ValueCountFrequency (%)
P 27880
14.0%
F 26362
13.2%
C 20585
 
10.3%
M 13361
 
6.7%
S 11911
 
6.0%
E 9746
 
4.9%
A 9547
 
4.8%
T 9356
 
4.7%
B 9001
 
4.5%
G 7811
 
3.9%
Other values (52) 53405
26.8%
Other Punctuation
ValueCountFrequency (%)
' 140657
75.6%
, 37354
 
20.1%
. 5671
 
3.0%
" 987
 
0.5%
& 764
 
0.4%
/ 645
 
0.3%
! 36
 
< 0.1%
% 18
 
< 0.1%
\ 12
 
< 0.1%
: 9
 
< 0.1%
Other values (6) 17
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1035
23.8%
1 712
16.3%
0 648
14.9%
3 556
12.8%
4 481
11.0%
9 205
 
4.7%
6 195
 
4.5%
5 178
 
4.1%
8 173
 
4.0%
7 172
 
3.9%
Open Punctuation
ValueCountFrequency (%)
[ 45385
91.3%
( 4318
 
8.7%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
] 45385
91.3%
) 4317
 
8.7%
1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 4329
> 99.9%
2
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+ 661
99.8%
| 1
 
0.2%
Other Symbol
ValueCountFrequency (%)
° 23
92.0%
2
 
8.0%
Final Punctuation
ValueCountFrequency (%)
3
50.0%
» 3
50.0%
Other Number
ValueCountFrequency (%)
² 1
50.0%
½ 1
50.0%
Space Separator
ValueCountFrequency (%)
144110
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Initial Punctuation
ValueCountFrequency (%)
« 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1185596
73.0%
Common 439073
 
27.0%
Cyrillic 373
 
< 0.1%
Hangul 115
 
< 0.1%
Greek 31
 
< 0.1%
Han 26
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 106938
 
9.0%
e 94644
 
8.0%
n 89969
 
7.6%
o 85292
 
7.2%
r 83547
 
7.0%
t 83437
 
7.0%
a 77148
 
6.5%
s 62667
 
5.3%
l 51264
 
4.3%
m 44275
 
3.7%
Other values (99) 406415
34.3%
Hangul
ValueCountFrequency (%)
9
 
7.8%
8
 
7.0%
6
 
5.2%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
5
 
4.3%
4
 
3.5%
3
 
2.6%
Other values (43) 60
52.2%
Cyrillic
ValueCountFrequency (%)
и 34
 
9.1%
о 28
 
7.5%
а 26
 
7.0%
л 22
 
5.9%
н 20
 
5.4%
м 19
 
5.1%
т 17
 
4.6%
е 16
 
4.3%
с 16
 
4.3%
ь 16
 
4.3%
Other values (36) 159
42.6%
Common
ValueCountFrequency (%)
144110
32.8%
' 140657
32.0%
[ 45385
 
10.3%
] 45385
 
10.3%
, 37354
 
8.5%
. 5671
 
1.3%
- 4329
 
1.0%
( 4318
 
1.0%
) 4317
 
1.0%
2 1035
 
0.2%
Other values (34) 6512
 
1.5%
Greek
ValueCountFrequency (%)
ο 3
 
9.7%
ν 3
 
9.7%
Ε 2
 
6.5%
λ 2
 
6.5%
η 2
 
6.5%
ι 2
 
6.5%
ρ 2
 
6.5%
Κ 2
 
6.5%
τ 2
 
6.5%
υ 1
 
3.2%
Other values (10) 10
32.3%
Han
ValueCountFrequency (%)
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (9) 9
34.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1618990
99.6%
None 5706
 
0.4%
Cyrillic 373
 
< 0.1%
Hangul 113
 
< 0.1%
CJK 26
 
< 0.1%
Punctuation 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
144110
 
8.9%
' 140657
 
8.7%
i 106938
 
6.6%
e 94644
 
5.8%
n 89969
 
5.6%
o 85292
 
5.3%
r 83547
 
5.2%
t 83437
 
5.2%
a 77148
 
4.8%
s 62667
 
3.9%
Other values (76) 650581
40.2%
None
ValueCountFrequency (%)
é 3176
55.7%
ó 416
 
7.3%
á 317
 
5.6%
í 173
 
3.0%
ü 154
 
2.7%
ñ 150
 
2.6%
ô 140
 
2.5%
ä 137
 
2.4%
è 136
 
2.4%
ö 132
 
2.3%
Other values (75) 775
 
13.6%
Cyrillic
ValueCountFrequency (%)
и 34
 
9.1%
о 28
 
7.5%
а 26
 
7.0%
л 22
 
5.9%
н 20
 
5.4%
м 19
 
5.1%
т 17
 
4.6%
е 16
 
4.3%
с 16
 
4.3%
ь 16
 
4.3%
Other values (36) 159
42.6%
Hangul
ValueCountFrequency (%)
9
 
8.0%
8
 
7.1%
6
 
5.3%
5
 
4.4%
5
 
4.4%
5
 
4.4%
5
 
4.4%
5
 
4.4%
4
 
3.5%
3
 
2.7%
Other values (42) 58
51.3%
Punctuation
ValueCountFrequency (%)
3
50.0%
2
33.3%
1
 
16.7%
CJK
ValueCountFrequency (%)
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (9) 9
34.6%
Distinct2389
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:45.731529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length289
Median length199
Mean length20.617419
Min length2

Characters and Unicode

Total characters935536
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1764 ?
Unique (%)3.9%

Sample

1st row['United States of America']
2nd row['United States of America']
3rd row['United States of America']
4th row['United States of America']
5th row['United States of America']
ValueCountFrequency (%)
united 25266
20.2%
states 21148
16.9%
of 21147
16.9%
america 21147
16.9%
6211
 
5.0%
kingdom 4091
 
3.3%
france 3939
 
3.2%
germany 2260
 
1.8%
italy 2168
 
1.7%
canada 1765
 
1.4%
Other values (178) 15823
12.7%
2024-07-04T18:47:46.074300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 98811
 
10.6%
e 80649
 
8.6%
79589
 
8.5%
t 72619
 
7.8%
a 70488
 
7.5%
i 58548
 
6.3%
n 47495
 
5.1%
] 45376
 
4.9%
[ 45376
 
4.9%
d 34545
 
3.7%
Other values (46) 302040
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 558562
59.7%
Other Punctuation 109064
 
11.7%
Uppercase Letter 97569
 
10.4%
Space Separator 79589
 
8.5%
Close Punctuation 45376
 
4.9%
Open Punctuation 45376
 
4.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 80649
14.4%
t 72619
13.0%
a 70488
12.6%
i 58548
10.5%
n 47495
8.5%
d 34545
6.2%
r 32490
5.8%
o 29580
 
5.3%
m 28704
 
5.1%
c 26371
 
4.7%
Other values (16) 77073
13.8%
Uppercase Letter
ValueCountFrequency (%)
U 25367
26.0%
S 23836
24.4%
A 22389
22.9%
K 5218
 
5.3%
F 4334
 
4.4%
I 3585
 
3.7%
C 2594
 
2.7%
G 2473
 
2.5%
J 1664
 
1.7%
R 1307
 
1.3%
Other values (14) 4802
 
4.9%
Other Punctuation
ValueCountFrequency (%)
' 98811
90.6%
, 10243
 
9.4%
" 10
 
< 0.1%
Space Separator
ValueCountFrequency (%)
79589
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45376
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 45376
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 656131
70.1%
Common 279405
29.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 80649
12.3%
t 72619
11.1%
a 70488
10.7%
i 58548
 
8.9%
n 47495
 
7.2%
d 34545
 
5.3%
r 32490
 
5.0%
o 29580
 
4.5%
m 28704
 
4.4%
c 26371
 
4.0%
Other values (40) 174642
26.6%
Common
ValueCountFrequency (%)
' 98811
35.4%
79589
28.5%
] 45376
16.2%
[ 45376
16.2%
, 10243
 
3.7%
" 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 935536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 98811
 
10.6%
e 80649
 
8.6%
79589
 
8.5%
t 72619
 
7.8%
a 70488
 
7.5%
i 58548
 
6.3%
n 47495
 
5.1%
] 45376
 
4.9%
[ 45376
 
4.9%
d 34545
 
3.7%
Other values (46) 302040
32.3%
Distinct17333
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
Minimum1874-12-09 00:00:00
Maximum2020-12-16 00:00:00
2024-07-04T18:47:46.194576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:46.303273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

revenue
Real number (ℝ)

ZEROS 

Distinct6863
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11230099
Minimum0
Maximum2.7879651 × 109
Zeros37969
Zeros (%)83.7%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:46.394108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile48020044
Maximum2.7879651 × 109
Range2.7879651 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation64389957
Coefficient of variation (CV)5.7336944
Kurtosis237.07741
Mean11230099
Median Absolute Deviation (MAD)0
Skewness12.254722
Sum5.0957698 × 1011
Variance4.1460665 × 1015
MonotonicityNot monotonic
2024-07-04T18:47:46.501259image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37969
83.7%
12000000 20
 
< 0.1%
10000000 19
 
< 0.1%
11000000 19
 
< 0.1%
2000000 18
 
< 0.1%
6000000 17
 
< 0.1%
5000000 14
 
< 0.1%
8000000 13
 
< 0.1%
500000 13
 
< 0.1%
1 12
 
< 0.1%
Other values (6853) 7262
 
16.0%
ValueCountFrequency (%)
0 37969
83.7%
1 12
 
< 0.1%
2 3
 
< 0.1%
3 9
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 4
 
< 0.1%
8 5
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
2787965087 1
< 0.1%
2068223624 1
< 0.1%
1845034188 1
< 0.1%
1519557910 1
< 0.1%
1513528810 1
< 0.1%
1506249360 1
< 0.1%
1405403694 1
< 0.1%
1342000000 1
< 0.1%
1274219009 1
< 0.1%
1262886337 1
< 0.1%

runtime
Real number (ℝ)

ZEROS 

Distinct353
Distinct (%)0.8%
Missing246
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean94.181675
Minimum0
Maximum1256
Zeros1535
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:46.610278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q185
median95
Q3107
95-th percentile138
Maximum1256
Range1256
Interquartile range (IQR)22

Descriptive statistics

Standard deviation38.341059
Coefficient of variation (CV)0.4070968
Kurtosis93.925543
Mean94.181675
Median Absolute Deviation (MAD)11
Skewness4.4907363
Sum4250419
Variance1470.0368
MonotonicityNot monotonic
2024-07-04T18:47:46.714587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2549
 
5.6%
0 1535
 
3.4%
100 1470
 
3.2%
95 1410
 
3.1%
93 1214
 
2.7%
96 1104
 
2.4%
92 1079
 
2.4%
94 1062
 
2.3%
91 1055
 
2.3%
88 1030
 
2.3%
Other values (343) 31622
69.7%
ValueCountFrequency (%)
0 1535
3.4%
1 107
 
0.2%
2 33
 
0.1%
3 48
 
0.1%
4 50
 
0.1%
5 51
 
0.1%
6 72
 
0.2%
7 103
 
0.2%
8 78
 
0.2%
9 63
 
0.1%
ValueCountFrequency (%)
1256 1
< 0.1%
1140 2
< 0.1%
931 1
< 0.1%
925 1
< 0.1%
900 1
< 0.1%
877 1
< 0.1%
874 1
< 0.1%
840 2
< 0.1%
780 1
< 0.1%
720 1
< 0.1%
Distinct1843
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:46.874531image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length214
Median length11
Mean length12.941819
Min length2

Characters and Unicode

Total characters587248
Distinct characters176
Distinct categories10 ?
Distinct scripts15 ?
Distinct blocks16 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1293 ?
Unique (%)2.8%

Sample

1st row['English']
2nd row['English', 'Français']
3rd row['English']
4th row['English']
5th row['English']
ValueCountFrequency (%)
english 28729
49.1%
4748
 
8.1%
français 4194
 
7.2%
deutsch 2624
 
4.5%
español 2412
 
4.1%
italiano 2366
 
4.0%
日本語 1758
 
3.0%
pусский 1562
 
2.7%
普通话 790
 
1.4%
हिन्दी 707
 
1.2%
Other values (69) 8565
 
14.7%
2024-07-04T18:47:47.156261image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 106548
18.1%
[ 45376
 
7.7%
] 45376
 
7.7%
s 42270
 
7.2%
n 37462
 
6.4%
i 37109
 
6.3%
l 34631
 
5.9%
h 31459
 
5.4%
E 31198
 
5.3%
g 30413
 
5.2%
Other values (166) 145406
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 292080
49.7%
Other Punctuation 119305
20.3%
Uppercase Letter 46428
 
7.9%
Open Punctuation 45376
 
7.7%
Close Punctuation 45376
 
7.7%
Other Letter 22191
 
3.8%
Space Separator 13079
 
2.2%
Spacing Mark 1838
 
0.3%
Nonspacing Mark 1549
 
0.3%
Decimal Number 26
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 42270
14.5%
n 37462
12.8%
i 37109
12.7%
l 34631
11.9%
h 31459
10.8%
g 30413
10.4%
a 18972
6.5%
o 7053
 
2.4%
r 6128
 
2.1%
t 5977
 
2.0%
Other values (64) 40606
13.9%
Other Letter
ValueCountFrequency (%)
1758
 
7.9%
1758
 
7.9%
1758
 
7.9%
1263
 
5.7%
946
 
4.3%
790
 
3.6%
790
 
3.6%
707
 
3.2%
707
 
3.2%
707
 
3.2%
Other values (46) 11007
49.6%
Uppercase Letter
ValueCountFrequency (%)
E 31198
67.2%
F 4196
 
9.0%
D 2926
 
6.3%
P 2677
 
5.8%
I 2366
 
5.1%
N 829
 
1.8%
L 505
 
1.1%
M 362
 
0.8%
T 308
 
0.7%
Č 284
 
0.6%
Other values (13) 777
 
1.7%
Spacing Mark
ValueCountFrequency (%)
707
38.5%
ि 707
38.5%
136
 
7.4%
ி 111
 
6.0%
94
 
5.1%
47
 
2.6%
18
 
1.0%
18
 
1.0%
Nonspacing Mark
ValueCountFrequency (%)
707
45.6%
ִ 430
27.8%
ְ 215
 
13.9%
111
 
7.2%
68
 
4.4%
18
 
1.2%
Other Punctuation
ValueCountFrequency (%)
' 106548
89.3%
, 11666
 
9.8%
/ 1015
 
0.9%
? 50
 
< 0.1%
\ 26
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
[ 45376
100.0%
Close Punctuation
ValueCountFrequency (%)
] 45376
100.0%
Space Separator
ValueCountFrequency (%)
13079
100.0%
Decimal Number
ValueCountFrequency (%)
9 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 326119
55.5%
Common 223162
38.0%
Han 10482
 
1.8%
Cyrillic 10454
 
1.8%
Devanagari 4242
 
0.7%
Arabic 3344
 
0.6%
Hangul 3252
 
0.6%
Hebrew 1720
 
0.3%
Greek 1704
 
0.3%
Thai 1232
 
0.2%
Other values (5) 1537
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 42270
13.0%
n 37462
11.5%
i 37109
11.4%
l 34631
10.6%
h 31459
9.6%
E 31198
9.6%
g 30413
9.3%
a 18972
 
5.8%
o 7053
 
2.2%
r 6128
 
1.9%
Other values (51) 49424
15.2%
Cyrillic
ValueCountFrequency (%)
с 3211
30.7%
к 1734
16.6%
и 1679
16.1%
й 1615
15.4%
у 1564
15.0%
а 113
 
1.1%
р 87
 
0.8%
У 53
 
0.5%
ь 53
 
0.5%
н 53
 
0.5%
Other values (12) 292
 
2.8%
Arabic
ValueCountFrequency (%)
ر 537
16.1%
ا 537
16.1%
ي 341
10.2%
ة 341
10.2%
ب 341
10.2%
ع 341
10.2%
ل 341
10.2%
ف 141
 
4.2%
ی 141
 
4.2%
س 141
 
4.2%
Other values (5) 142
 
4.2%
Han
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
广 473
 
4.5%
473
 
4.5%
473
 
4.5%
Common
ValueCountFrequency (%)
' 106548
47.7%
[ 45376
20.3%
] 45376
20.3%
13079
 
5.9%
, 11666
 
5.2%
/ 1015
 
0.5%
? 50
 
< 0.1%
9 26
 
< 0.1%
\ 26
 
< 0.1%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
ת 215
12.5%
י 215
12.5%
ר 215
12.5%
ְ 215
12.5%
ב 215
12.5%
ע 215
12.5%
Greek
ValueCountFrequency (%)
λ 426
25.0%
ά 213
12.5%
κ 213
12.5%
ε 213
12.5%
ι 213
12.5%
ν 213
12.5%
η 213
12.5%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Devanagari
ValueCountFrequency (%)
707
16.7%
707
16.7%
707
16.7%
ि 707
16.7%
707
16.7%
707
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Thai
ValueCountFrequency (%)
352
28.6%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540451
92.0%
CJK 10482
 
1.8%
Cyrillic 10454
 
1.8%
None 10408
 
1.8%
Devanagari 4242
 
0.7%
Arabic 3344
 
0.6%
Hangul 3252
 
0.6%
Hebrew 1720
 
0.3%
Thai 1232
 
0.2%
Tamil 555
 
0.1%
Other values (6) 1108
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 106548
19.7%
[ 45376
8.4%
] 45376
8.4%
s 42270
 
7.8%
n 37462
 
6.9%
i 37109
 
6.9%
l 34631
 
6.4%
h 31459
 
5.8%
E 31198
 
5.8%
g 30413
 
5.6%
Other values (44) 98609
18.2%
None
ValueCountFrequency (%)
ç 4441
42.7%
ñ 2412
23.2%
ê 591
 
5.7%
λ 426
 
4.1%
Č 284
 
2.7%
ý 284
 
2.7%
ü 247
 
2.4%
ά 213
 
2.0%
κ 213
 
2.0%
ε 213
 
2.0%
Other values (10) 1084
 
10.4%
Cyrillic
ValueCountFrequency (%)
с 3211
30.7%
к 1734
16.6%
и 1679
16.1%
й 1615
15.4%
у 1564
15.0%
а 113
 
1.1%
р 87
 
0.8%
У 53
 
0.5%
ь 53
 
0.5%
н 53
 
0.5%
Other values (12) 292
 
2.8%
CJK
ValueCountFrequency (%)
1758
16.8%
1758
16.8%
1758
16.8%
1263
12.0%
946
9.0%
790
7.5%
790
7.5%
广 473
 
4.5%
473
 
4.5%
473
 
4.5%
Devanagari
ValueCountFrequency (%)
707
16.7%
707
16.7%
707
16.7%
ि 707
16.7%
707
16.7%
707
16.7%
Hangul
ValueCountFrequency (%)
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
542
16.7%
Arabic
ValueCountFrequency (%)
ر 537
16.1%
ا 537
16.1%
ي 341
10.2%
ة 341
10.2%
ب 341
10.2%
ع 341
10.2%
ل 341
10.2%
ف 141
 
4.2%
ی 141
 
4.2%
س 141
 
4.2%
Other values (5) 142
 
4.2%
Hebrew
ValueCountFrequency (%)
ִ 430
25.0%
ת 215
12.5%
י 215
12.5%
ר 215
12.5%
ְ 215
12.5%
ב 215
12.5%
ע 215
12.5%
Thai
ValueCountFrequency (%)
352
28.6%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
176
14.3%
Telugu
ValueCountFrequency (%)
136
33.3%
68
16.7%
68
16.7%
68
16.7%
68
16.7%
Tamil
ValueCountFrequency (%)
111
20.0%
ி 111
20.0%
111
20.0%
111
20.0%
111
20.0%
Bengali
ValueCountFrequency (%)
94
40.0%
47
20.0%
47
20.0%
47
20.0%
Latin Ext Additional
ValueCountFrequency (%)
ế 61
50.0%
61
50.0%
Georgian
ValueCountFrequency (%)
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
33
14.3%
Gurmukhi
ValueCountFrequency (%)
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
18
16.7%
IPA Ext
ValueCountFrequency (%)
ə 4
100.0%

status
Categorical

IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing80
Missing (%)0.2%
Memory size354.6 KiB
Released
44936 
Rumored
 
230
Post Production
 
97
In Production
 
19
Planned
 
13

Length

Max length15
Median length8
Mean length8.0117229
Min length7

Characters and Unicode

Total characters362899
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowReleased
2nd rowReleased
3rd rowReleased
4th rowReleased
5th rowReleased

Common Values

ValueCountFrequency (%)
Released 44936
99.0%
Rumored 230
 
0.5%
Post Production 97
 
0.2%
In Production 19
 
< 0.1%
Planned 13
 
< 0.1%
Canceled 1
 
< 0.1%
(Missing) 80
 
0.2%

Length

2024-07-04T18:47:47.269760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-04T18:47:47.354738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
released 44936
99.0%
rumored 230
 
0.5%
production 116
 
0.3%
post 97
 
0.2%
in 19
 
< 0.1%
planned 13
 
< 0.1%
canceled 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 317371
87.5%
Uppercase Letter 45412
 
12.5%
Space Separator 116
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 135053
42.6%
d 45296
 
14.3%
s 45033
 
14.2%
l 44950
 
14.2%
a 44950
 
14.2%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
t 213
 
0.1%
Other values (3) 395
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
R 45166
99.5%
P 226
 
0.5%
I 19
 
< 0.1%
C 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 362783
> 99.9%
Common 116
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (7) 854
 
0.2%
Common
ValueCountFrequency (%)
116
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 362899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 135053
37.2%
d 45296
 
12.5%
R 45166
 
12.4%
s 45033
 
12.4%
l 44950
 
12.4%
a 44950
 
12.4%
o 559
 
0.2%
r 346
 
0.1%
u 346
 
0.1%
m 230
 
0.1%
Other values (8) 970
 
0.3%

tagline
Text

MISSING 

Distinct20269
Distinct (%)99.4%
Missing24978
Missing (%)55.0%
Memory size354.6 KiB
2024-07-04T18:47:47.608253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length297
Median length204
Mean length46.999314
Min length1

Characters and Unicode

Total characters958692
Distinct characters170
Distinct categories17 ?
Distinct scripts6 ?
Distinct blocks10 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20163 ?
Unique (%)98.8%

Sample

1st rowRoll the dice and unleash the excitement!
2nd rowStill Yelling. Still Fighting. Still Ready for Love.
3rd rowFriends are the people who let you be yourself... and never let you forget it.
4th rowJust When His World Is Back To Normal... He's In For The Surprise Of His Life!
5th rowA Los Angeles Crime Saga
ValueCountFrequency (%)
the 10998
 
6.3%
a 6815
 
3.9%
of 4404
 
2.5%
to 3584
 
2.1%
is 2796
 
1.6%
in 2693
 
1.5%
and 2682
 
1.5%
you 2389
 
1.4%
1582
 
0.9%
for 1523
 
0.9%
Other values (15100) 134470
77.3%
2024-07-04T18:47:47.995010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.8%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (160) 327230
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 680479
71.0%
Space Separator 153686
 
16.0%
Uppercase Letter 74991
 
7.8%
Other Punctuation 44585
 
4.7%
Decimal Number 2687
 
0.3%
Dash Punctuation 1944
 
0.2%
Final Punctuation 98
 
< 0.1%
Open Punctuation 56
 
< 0.1%
Close Punctuation 55
 
< 0.1%
Currency Symbol 37
 
< 0.1%
Other values (7) 74
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 94412
13.9%
t 57267
 
8.4%
o 56566
 
8.3%
a 51473
 
7.6%
n 47498
 
7.0%
i 46036
 
6.8%
r 44992
 
6.6%
s 42360
 
6.2%
h 37172
 
5.5%
l 30174
 
4.4%
Other values (43) 172529
25.4%
Other Letter
ValueCountFrequency (%)
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
1
 
2.9%
Other values (24) 24
70.6%
Uppercase Letter
ValueCountFrequency (%)
T 10009
 
13.3%
A 6874
 
9.2%
S 5652
 
7.5%
H 4402
 
5.9%
I 4387
 
5.9%
E 4306
 
5.7%
W 3681
 
4.9%
O 3477
 
4.6%
N 3195
 
4.3%
L 3194
 
4.3%
Other values (20) 25814
34.4%
Other Punctuation
ValueCountFrequency (%)
. 26647
59.8%
! 5784
 
13.0%
' 5674
 
12.7%
, 4226
 
9.5%
? 1161
 
2.6%
" 582
 
1.3%
148
 
0.3%
: 138
 
0.3%
& 83
 
0.2%
* 42
 
0.1%
Other values (7) 100
 
0.2%
Decimal Number
ValueCountFrequency (%)
0 802
29.8%
1 516
19.2%
2 299
 
11.1%
3 208
 
7.7%
9 208
 
7.7%
5 168
 
6.3%
4 140
 
5.2%
6 121
 
4.5%
7 121
 
4.5%
8 104
 
3.9%
Math Symbol
ValueCountFrequency (%)
+ 5
35.7%
= 5
35.7%
| 2
 
14.3%
~ 1
 
7.1%
1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 1927
99.1%
9
 
0.5%
8
 
0.4%
Final Punctuation
ValueCountFrequency (%)
82
83.7%
15
 
15.3%
» 1
 
1.0%
Initial Punctuation
ValueCountFrequency (%)
14
73.7%
4
 
21.1%
« 1
 
5.3%
Open Punctuation
ValueCountFrequency (%)
( 49
87.5%
[ 7
 
12.5%
Close Punctuation
ValueCountFrequency (%)
) 48
87.3%
] 7
 
12.7%
Other Number
ValueCountFrequency (%)
½ 2
66.7%
² 1
33.3%
Modifier Letter
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Space Separator
ValueCountFrequency (%)
153686
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 37
100.0%
Nonspacing Mark
ValueCountFrequency (%)
1
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 755470
78.8%
Common 203187
 
21.2%
Han 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 94412
 
12.5%
t 57267
 
7.6%
o 56566
 
7.5%
a 51473
 
6.8%
n 47498
 
6.3%
i 46036
 
6.1%
r 44992
 
6.0%
s 42360
 
5.6%
h 37172
 
4.9%
l 30174
 
4.0%
Other values (73) 247520
32.8%
Common
ValueCountFrequency (%)
153686
75.6%
. 26647
 
13.1%
! 5784
 
2.8%
' 5674
 
2.8%
, 4226
 
2.1%
- 1927
 
0.9%
? 1161
 
0.6%
0 802
 
0.4%
" 582
 
0.3%
1 516
 
0.3%
Other values (42) 2182
 
1.1%
Han
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 958262
> 99.9%
Punctuation 280
 
< 0.1%
None 110
 
< 0.1%
CJK 21
 
< 0.1%
Tamil 5
 
< 0.1%
Hiragana 5
 
< 0.1%
Katakana 4
 
< 0.1%
IPA Ext 2
 
< 0.1%
Modifier Letters 2
 
< 0.1%
Math Operators 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
153686
16.0%
e 94412
 
9.9%
t 57267
 
6.0%
o 56566
 
5.9%
a 51473
 
5.4%
n 47498
 
5.0%
i 46036
 
4.8%
r 44992
 
4.7%
s 42360
 
4.4%
h 37172
 
3.9%
Other values (78) 326800
34.1%
Punctuation
ValueCountFrequency (%)
148
52.9%
82
29.3%
15
 
5.4%
14
 
5.0%
9
 
3.2%
8
 
2.9%
4
 
1.4%
None
ValueCountFrequency (%)
é 18
16.4%
ä 16
14.5%
ö 8
 
7.3%
á 6
 
5.5%
ó 6
 
5.5%
ü 5
 
4.5%
í 5
 
4.5%
ı 5
 
4.5%
· 4
 
3.6%
ć 3
 
2.7%
Other values (26) 34
30.9%
IPA Ext
ValueCountFrequency (%)
ə 2
100.0%
Tamil
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
CJK
ValueCountFrequency (%)
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (11) 11
52.4%
Katakana
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Modifier Letters
ValueCountFrequency (%)
ˌ 1
50.0%
ˈ 1
50.0%
Hiragana
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

title
Text

Distinct42196
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:48.274806image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length105
Median length79
Mean length16.701781
Min length1

Characters and Unicode

Total characters757860
Distinct characters287
Distinct categories17 ?
Distinct scripts7 ?
Distinct blocks12 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39869 ?
Unique (%)87.9%

Sample

1st rowToy Story
2nd rowJumanji
3rd rowGrumpier Old Men
4th rowWaiting to Exhale
5th rowFather of the Bride Part II
ValueCountFrequency (%)
the 14555
 
10.7%
of 4930
 
3.6%
a 2241
 
1.6%
in 1693
 
1.2%
and 1631
 
1.2%
to 1054
 
0.8%
757
 
0.6%
man 665
 
0.5%
love 664
 
0.5%
for 601
 
0.4%
Other values (24353) 107390
78.9%
2024-07-04T18:47:48.732056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.2%
t 36722
 
4.8%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (277) 280815
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534134
70.5%
Uppercase Letter 117265
 
15.5%
Space Separator 90827
 
12.0%
Other Punctuation 10489
 
1.4%
Decimal Number 3850
 
0.5%
Dash Punctuation 981
 
0.1%
Close Punctuation 87
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Final Punctuation 38
 
< 0.1%
Other Letter 25
 
< 0.1%
Other values (7) 79
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 76251
14.3%
a 48940
9.2%
o 45671
 
8.6%
n 40817
 
7.6%
r 40018
 
7.5%
i 39764
 
7.4%
t 36722
 
6.9%
s 29519
 
5.5%
h 28516
 
5.3%
l 25924
 
4.9%
Other values (121) 121992
22.8%
Uppercase Letter
ValueCountFrequency (%)
T 16019
13.7%
S 10336
 
8.8%
M 8031
 
6.8%
B 7659
 
6.5%
C 7165
 
6.1%
A 6785
 
5.8%
D 6335
 
5.4%
L 5872
 
5.0%
H 5170
 
4.4%
W 5166
 
4.4%
Other values (65) 38727
33.0%
Other Letter
ValueCountFrequency (%)
چ 2
 
8.0%
ه 2
 
8.0%
ی 2
 
8.0%
ک 2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
ª 1
 
4.0%
Other values (11) 11
44.0%
Other Punctuation
ValueCountFrequency (%)
: 3717
35.4%
' 2505
23.9%
. 1603
15.3%
, 1134
 
10.8%
! 647
 
6.2%
& 458
 
4.4%
? 269
 
2.6%
/ 79
 
0.8%
* 19
 
0.2%
# 13
 
0.1%
Other values (8) 45
 
0.4%
Decimal Number
ValueCountFrequency (%)
2 861
22.4%
1 697
18.1%
0 616
16.0%
3 482
12.5%
9 230
 
6.0%
4 229
 
5.9%
5 225
 
5.8%
7 193
 
5.0%
8 161
 
4.2%
6 156
 
4.1%
Math Symbol
ValueCountFrequency (%)
+ 17
70.8%
× 3
 
12.5%
1
 
4.2%
= 1
 
4.2%
1
 
4.2%
1
 
4.2%
Other Number
ValueCountFrequency (%)
½ 12
63.2%
² 3
 
15.8%
³ 2
 
10.5%
1
 
5.3%
1
 
5.3%
Other Symbol
ValueCountFrequency (%)
° 3
37.5%
2
25.0%
1
 
12.5%
1
 
12.5%
1
 
12.5%
Currency Symbol
ValueCountFrequency (%)
$ 18
85.7%
¢ 2
 
9.5%
£ 1
 
4.8%
Dash Punctuation
ValueCountFrequency (%)
- 966
98.5%
15
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 82
94.3%
] 5
 
5.7%
Open Punctuation
ValueCountFrequency (%)
( 80
94.1%
[ 5
 
5.9%
Final Punctuation
ValueCountFrequency (%)
37
97.4%
1
 
2.6%
Initial Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
90827
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Format
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 650884
85.9%
Common 106436
 
14.0%
Cyrillic 346
 
< 0.1%
Greek 170
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
Han 5
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 76251
 
11.7%
a 48940
 
7.5%
o 45671
 
7.0%
n 40817
 
6.3%
r 40018
 
6.1%
i 39764
 
6.1%
t 36722
 
5.6%
s 29519
 
4.5%
h 28516
 
4.4%
l 25924
 
4.0%
Other values (107) 238742
36.7%
Common
ValueCountFrequency (%)
90827
85.3%
: 3717
 
3.5%
' 2505
 
2.4%
. 1603
 
1.5%
, 1134
 
1.1%
- 966
 
0.9%
2 861
 
0.8%
1 697
 
0.7%
! 647
 
0.6%
0 616
 
0.6%
Other values (50) 2863
 
2.7%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
в 14
 
4.0%
Other values (38) 124
35.8%
Greek
ValueCountFrequency (%)
α 20
 
11.8%
ι 14
 
8.2%
ο 14
 
8.2%
τ 9
 
5.3%
ά 8
 
4.7%
λ 8
 
4.7%
ρ 8
 
4.7%
ν 7
 
4.1%
ε 6
 
3.5%
ς 6
 
3.5%
Other values (32) 70
41.2%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Han
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756295
99.8%
None 1124
 
0.1%
Cyrillic 346
 
< 0.1%
Punctuation 62
 
< 0.1%
Arabic 11
 
< 0.1%
Katakana 8
 
< 0.1%
CJK 5
 
< 0.1%
Misc Symbols 3
 
< 0.1%
Letterlike Symbols 2
 
< 0.1%
Math Operators 2
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
90827
 
12.0%
e 76251
 
10.1%
a 48940
 
6.5%
o 45671
 
6.0%
n 40817
 
5.4%
r 40018
 
5.3%
i 39764
 
5.3%
t 36722
 
4.9%
s 29519
 
3.9%
h 28516
 
3.8%
Other values (76) 279250
36.9%
None
ValueCountFrequency (%)
é 218
19.4%
ä 127
 
11.3%
ö 55
 
4.9%
è 53
 
4.7%
ô 44
 
3.9%
ü 39
 
3.5%
ó 37
 
3.3%
á 35
 
3.1%
ı 35
 
3.1%
í 33
 
2.9%
Other values (108) 448
39.9%
Punctuation
ValueCountFrequency (%)
37
59.7%
15
24.2%
5
 
8.1%
2
 
3.2%
1
 
1.6%
1
 
1.6%
1
 
1.6%
Cyrillic
ValueCountFrequency (%)
е 32
 
9.2%
о 32
 
9.2%
а 29
 
8.4%
н 24
 
6.9%
и 23
 
6.6%
р 22
 
6.4%
к 17
 
4.9%
с 15
 
4.3%
л 14
 
4.0%
в 14
 
4.0%
Other values (38) 124
35.8%
Arabic
ValueCountFrequency (%)
چ 2
18.2%
ه 2
18.2%
ی 2
18.2%
ک 2
18.2%
س 1
9.1%
ا 1
9.1%
ج 1
9.1%
Misc Symbols
ValueCountFrequency (%)
2
66.7%
1
33.3%
CJK
ValueCountFrequency (%)
1
20.0%
1
20.0%
1
20.0%
1
20.0%
1
20.0%
Number Forms
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
50.0%
1
50.0%
Math Operators
ValueCountFrequency (%)
1
50.0%
1
50.0%
Katakana
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Arrows
ValueCountFrequency (%)
1
100.0%

vote_average
Real number (ℝ)

ZEROS 

Distinct92
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.62407
Minimum0
Maximum10
Zeros2947
Zeros (%)6.5%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:48.876681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median6
Q36.8
95-th percentile7.8
Maximum10
Range10
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.9154225
Coefficient of variation (CV)0.34057587
Kurtosis2.5420547
Mean5.62407
Median Absolute Deviation (MAD)0.9
Skewness-1.524472
Sum255197.8
Variance3.6688434
MonotonicityNot monotonic
2024-07-04T18:47:49.027246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2947
 
6.5%
6 2462
 
5.4%
5 1998
 
4.4%
7 1883
 
4.1%
6.5 1722
 
3.8%
6.3 1603
 
3.5%
5.5 1381
 
3.0%
5.8 1369
 
3.0%
6.4 1350
 
3.0%
6.7 1342
 
3.0%
Other values (82) 27319
60.2%
ValueCountFrequency (%)
0 2947
6.5%
0.5 13
 
< 0.1%
0.7 1
 
< 0.1%
1 103
 
0.2%
1.1 1
 
< 0.1%
1.2 4
 
< 0.1%
1.3 13
 
< 0.1%
1.4 5
 
< 0.1%
1.5 30
 
0.1%
1.6 6
 
< 0.1%
ValueCountFrequency (%)
10 185
0.4%
9.8 1
 
< 0.1%
9.6 1
 
< 0.1%
9.5 18
 
< 0.1%
9.4 3
 
< 0.1%
9.3 18
 
< 0.1%
9.2 4
 
< 0.1%
9.1 2
 
< 0.1%
9 158
0.3%
8.9 7
 
< 0.1%

vote_count
Real number (ℝ)

ZEROS 

Distinct1820
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110.09644
Minimum0
Maximum14075
Zeros2849
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:49.138338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median10
Q334
95-th percentile434
Maximum14075
Range14075
Interquartile range (IQR)31

Descriptive statistics

Standard deviation491.74289
Coefficient of variation (CV)4.4664741
Kurtosis150.92858
Mean110.09644
Median Absolute Deviation (MAD)8
Skewness10.440782
Sum4995736
Variance241811.07
MonotonicityNot monotonic
2024-07-04T18:47:49.235430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3242
 
7.1%
2 3127
 
6.9%
0 2849
 
6.3%
3 2785
 
6.1%
4 2478
 
5.5%
5 2097
 
4.6%
6 1747
 
3.9%
7 1570
 
3.5%
8 1359
 
3.0%
9 1194
 
2.6%
Other values (1810) 22928
50.5%
ValueCountFrequency (%)
0 2849
6.3%
1 3242
7.1%
2 3127
6.9%
3 2785
6.1%
4 2478
5.5%
5 2097
4.6%
6 1747
3.9%
7 1570
3.5%
8 1359
3.0%
9 1194
 
2.6%
ValueCountFrequency (%)
14075 1
< 0.1%
12269 1
< 0.1%
12114 1
< 0.1%
12000 1
< 0.1%
11444 1
< 0.1%
11187 1
< 0.1%
10297 1
< 0.1%
10014 1
< 0.1%
9678 1
< 0.1%
9634 1
< 0.1%

release_year
Real number (ℝ)

Distinct135
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1991.8812
Minimum1874
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:49.350321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1874
5-th percentile1941
Q11978
median2001
Q32010
95-th percentile2015
Maximum2020
Range146
Interquartile range (IQR)32

Descriptive statistics

Standard deviation24.05536
Coefficient of variation (CV)0.012076704
Kurtosis0.84010576
Mean1991.8812
Median Absolute Deviation (MAD)12
Skewness-1.2248636
Sum90383601
Variance578.66033
MonotonicityNot monotonic
2024-07-04T18:47:49.461902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 1974
 
4.4%
2015 1905
 
4.2%
2013 1889
 
4.2%
2012 1722
 
3.8%
2011 1667
 
3.7%
2016 1604
 
3.5%
2009 1586
 
3.5%
2010 1501
 
3.3%
2008 1473
 
3.2%
2007 1320
 
2.9%
Other values (125) 28735
63.3%
ValueCountFrequency (%)
1874 1
 
< 0.1%
1878 1
 
< 0.1%
1883 1
 
< 0.1%
1887 1
 
< 0.1%
1888 2
 
< 0.1%
1890 5
 
< 0.1%
1891 6
< 0.1%
1892 3
 
< 0.1%
1893 1
 
< 0.1%
1894 13
< 0.1%
ValueCountFrequency (%)
2020 1
 
< 0.1%
2018 5
 
< 0.1%
2017 532
 
1.2%
2016 1604
3.5%
2015 1905
4.2%
2014 1974
4.4%
2013 1889
4.2%
2012 1722
3.8%
2011 1667
3.7%
2010 1501
3.3%

return
Real number (ℝ)

SKEWED  ZEROS 

Distinct5232
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean660.04278
Minimum0
Maximum12396383
Zeros39995
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size354.6 KiB
2024-07-04T18:47:49.578554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2.5355363
Maximum12396383
Range12396383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation74693.294
Coefficient of variation (CV)113.16432
Kurtosis20672.957
Mean660.04278
Median Absolute Deviation (MAD)0
Skewness138.32953
Sum29950101
Variance5.5790882 × 109
MonotonicityNot monotonic
2024-07-04T18:47:49.688982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39995
88.1%
1 20
 
< 0.1%
2 12
 
< 0.1%
4 11
 
< 0.1%
5 8
 
< 0.1%
3 7
 
< 0.1%
2.5 7
 
< 0.1%
1.333333333 7
 
< 0.1%
1.5 6
 
< 0.1%
7 4
 
< 0.1%
Other values (5222) 5299
 
11.7%
ValueCountFrequency (%)
0 39995
88.1%
5.217391304 × 10-71
 
< 0.1%
7.5 × 10-71
 
< 0.1%
9.375 × 10-71
 
< 0.1%
1.499133126 × 10-61
 
< 0.1%
1.8 × 10-61
 
< 0.1%
1.916666667 × 10-61
 
< 0.1%
3.5 × 10-61
 
< 0.1%
4 × 10-61
 
< 0.1%
5.111111111 × 10-61
 
< 0.1%
ValueCountFrequency (%)
12396383 1
< 0.1%
8500000 1
< 0.1%
4197476.625 1
< 0.1%
2755584 1
< 0.1%
1018619.283 1
< 0.1%
1000000 1
< 0.1%
26881.72043 1
< 0.1%
12890.38667 1
< 0.1%
5330.33945 1
< 0.1%
4133.333333 1
< 0.1%

Interactions

2024-07-04T18:47:40.511795image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:32.791369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.504075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:34.162635image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.027613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.711197image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.406290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.084650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.794082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.591776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:32.868861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.576110image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.416870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.097394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.790749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.487422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.165067image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.884545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.661861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:32.948934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.642640image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.485753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.178120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.857707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.551076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.245146image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.956407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.738559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.015593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.722597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.550483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.244506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.937762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.631124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.325012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.031872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.818870image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.101473image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.789538image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.630342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.324494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.017541image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.697721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.393697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.114058image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.899690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.175665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.869218image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.721766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.390852image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.086437image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.782665image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.473516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.191929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.967229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.255685image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.935913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.790668image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.470886image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.164291image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.845321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.551666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.258690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:41.045604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.335701image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:34.015975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.870645image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.553212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.244241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.924802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.631827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.351739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:41.125702image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:33.418465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:34.096532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:36.950764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:37.632497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:38.324229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.004843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:39.711469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-07-04T18:47:40.434065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-07-04T18:47:41.265771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-04T18:47:41.525666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-04T18:47:41.752545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

collection_namebudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn
0Toy Story Collection30000000.0['Animation', 'Comedy', 'Family']862enLed by Woody, Andy's toys live happily in his room until Andy's birthday brings Buzz Lightyear onto the scene. Afraid of losing his place in Andy's heart, Woody plots against Buzz. But when circumstances separate Buzz and Woody from their owner, the duo eventually learns to put aside their differences.21.946943['Pixar Animation Studios']['United States of America']1995-10-30373554033.081.0['English']ReleasedNaNToy Story7.75415.0199512.451801
1NaN65000000.0['Adventure', 'Fantasy', 'Family']8844enWhen siblings Judy and Peter discover an enchanted board game that opens the door to a magical world, they unwittingly invite Alan -- an adult who's been trapped inside the game for 26 years -- into their living room. Alan's only hope for freedom is to finish the game, which proves risky as all three find themselves running from giant rhinoceroses, evil monkeys and other terrifying creatures.17.015539['TriStar Pictures', 'Teitler Film', 'Interscope Communications']['United States of America']1995-12-15262797249.0104.0['English', 'Français']ReleasedRoll the dice and unleash the excitement!Jumanji6.92413.019954.043035
2Grumpy Old Men Collection0.0['Romance', 'Comedy']15602enA family wedding reignites the ancient feud between next-door neighbors and fishing buddies John and Max. Meanwhile, a sultry Italian divorcée opens a restaurant at the local bait shop, alarming the locals who worry she'll scare the fish away. But she's less interested in seafood than she is in cooking up a hot time with Max.11.712900['Warner Bros.', 'Lancaster Gate']['United States of America']1995-12-220.0101.0['English']ReleasedStill Yelling. Still Fighting. Still Ready for Love.Grumpier Old Men6.592.019950.000000
3NaN16000000.0['Comedy', 'Drama', 'Romance']31357enCheated on, mistreated and stepped on, the women are holding their breath, waiting for the elusive "good man" to break a string of less-than-stellar lovers. Friends and confidants Vannah, Bernie, Glo and Robin talk it all out, determined to find a better way to breathe.3.859495['Twentieth Century Fox Film Corporation']['United States of America']1995-12-2281452156.0127.0['English']ReleasedFriends are the people who let you be yourself... and never let you forget it.Waiting to Exhale6.134.019955.090760
4Father of the Bride Collection0.0['Comedy']11862enJust when George Banks has recovered from his daughter's wedding, he receives the news that she's pregnant ... and that George's wife, Nina, is expecting too. He was planning on selling their home, but that's a plan that -- like George -- will have to change with the arrival of both a grandchild and a kid of his own.8.387519['Sandollar Productions', 'Touchstone Pictures']['United States of America']1995-02-1076578911.0106.0['English']ReleasedJust When His World Is Back To Normal... He's In For The Surprise Of His Life!Father of the Bride Part II5.7173.019950.000000
5NaN60000000.0['Action', 'Crime', 'Drama', 'Thriller']949enObsessive master thief, Neil McCauley leads a top-notch crew on various insane heists throughout Los Angeles while a mentally unstable detective, Vincent Hanna pursues him without rest. Each man recognizes and respects the ability and the dedication of the other even though they are aware their cat-and-mouse game may end in violence.17.924927['Regency Enterprises', 'Forward Pass', 'Warner Bros.']['United States of America']1995-12-15187436818.0170.0['English', 'Español']ReleasedA Los Angeles Crime SagaHeat7.71886.019953.123947
6NaN58000000.0['Comedy', 'Romance']11860enAn ugly duckling having undergone a remarkable change, still harbors feelings for her crush: a carefree playboy, but not before his business-focused brother has something to say about it.6.677277['Paramount Pictures', 'Scott Rudin Productions', 'Mirage Enterprises', 'Sandollar Productions', 'Constellation Entertainment', 'Worldwide', 'Mont Blanc Entertainment GmbH']['Germany', 'United States of America']1995-12-150.0127.0['Français', 'English']ReleasedYou are cordially invited to the most surprising merger of the year.Sabrina6.2141.019950.000000
7NaN0.0['Action', 'Adventure', 'Drama', 'Family']45325enA mischievous young boy, Tom Sawyer, witnesses a murder by the deadly Injun Joe. Tom becomes friends with Huckleberry Finn, a boy with no future and no family. Tom has to choose between honoring a friendship or honoring an oath because the town alcoholic is accused of the murder. Tom and Huck go through several adventures trying to retrieve evidence.2.561161['Walt Disney Pictures']['United States of America']1995-12-220.097.0['English', 'Deutsch']ReleasedThe Original Bad Boys.Tom and Huck5.445.019950.000000
8NaN35000000.0['Action', 'Adventure', 'Thriller']9091enInternational action superstar Jean Claude Van Damme teams with Powers Boothe in a Tension-packed, suspense thriller, set against the back-drop of a Stanley Cup game.Van Damme portrays a father whose daughter is suddenly taken during a championship hockey game. With the captors demanding a billion dollars by game's end, Van Damme frantically sets a plan in motion to rescue his daughter and abort an impending explosion before the final buzzer...5.231580['Universal Pictures', 'Imperial Entertainment', 'Signature Entertainment']['United States of America']1995-12-2264350171.0106.0['English']ReleasedTerror goes into overtime.Sudden Death5.5174.019951.838576
9James Bond Collection58000000.0['Adventure', 'Action', 'Thriller']710enJames Bond must unmask the mysterious head of the Janus Syndicate and prevent the leader from utilizing the GoldenEye weapons system to inflict devastating revenge on Britain.14.686036['United Artists', 'Eon Productions']['United Kingdom', 'United States of America']1995-11-16352194034.0130.0['English', 'Pусский', 'Español']ReleasedNo limits. No fears. No substitutes.GoldenEye6.61194.019956.072311
collection_namebudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn
45366NaN0.0[]67179itSentenced to life imprisonment for illegal activities, Italian International member Giulio Manieri holds on to his political ideals while struggling against madness in the loneliness of his prison cell.0.225051[][]1972-01-010.090.0['Italiano']ReleasedNaNSt. Michael Had a Rooster6.03.019720.0
45367NaN0.0['Horror', 'Mystery', 'Thriller']84419enAn unsuccessful sculptor saves a madman named "The Creeper" from drowning. Seeing an opportunity for revenge, he tricks the psycho into murdering his critics.0.222814['Universal Pictures']['United States of America']1946-03-290.065.0['English']ReleasedMeet...The CREEPER!House of Horrors6.38.019460.0
45368NaN0.0['Mystery', 'Horror']390959enIn this true-crime documentary, we delve into the murder spree that was the inspiration for Joe Berlinger's "Book of Shadows: Blair Witch 2".0.076061[][]2000-10-220.045.0['English']ReleasedNaNShadow of the Blair Witch7.02.020000.0
45369NaN0.0['Horror']289923enA film archivist revisits the story of Rustin Parr, a hermit thought to have murdered seven children while under the possession of the Blair Witch.0.386450['Neptune Salad Entertainment', 'Pirie Productions']['United States of America']2000-10-030.030.0['English']ReleasedDo you know what happened 50 years before "The Blair Witch Project"?The Burkittsville 77.01.020000.0
45370NaN0.0['Science Fiction']222848enIt's the year 3000 AD. The world's most dangerous women are banished to a remote asteroid 45 million light years from earth. Kira Murphy doesn't belong; wrongfully accused of a crime she did not commit, she's thrown in this interplanetary prison and left to her own defenses. But Kira's a fighter, and soon she finds herself in the middle of a female gang war; where everyone wants a piece of the action... and a piece of her! "Caged Heat 3000" takes the Women-in-Prison genre to a whole new level... and a whole new galaxy!0.661558['Concorde-New Horizons']['United States of America']1995-01-010.085.0['English']ReleasedNaNCaged Heat 30003.51.019950.0
45371NaN0.0['Drama', 'Action', 'Romance']30840enYet another version of the classic epic, with enough variation to make it interesting. The story is the same, but some of the characters are quite different from the usual, in particular Uma Thurman's very special maid Marian. The photography is also great, giving the story a somewhat darker tone.5.683753['Westdeutscher Rundfunk (WDR)', 'Working Title Films', '20th Century Fox Television', 'CanWest Global Communications']['Canada', 'Germany', 'United Kingdom', 'United States of America']1991-05-130.0104.0['English']ReleasedNaNRobin Hood5.726.019910.0
45372NaN0.0['Drama']111109tlAn artist struggles to finish his work while a storyline about a cult plays in his head.0.178241['Sine Olivia']['Philippines']2011-11-170.0360.0['']ReleasedNaNCentury of Birthing9.03.020110.0
45373NaN0.0['Action', 'Drama', 'Thriller']67758enWhen one of her hits goes wrong, a professional assassin ends up with a suitcase full of a million dollars belonging to a mob boss ...0.903007['American World Pictures']['United States of America']2003-08-010.090.0['English']ReleasedA deadly game of wits.Betrayal3.86.020030.0
45374NaN0.0[]227506enIn a small town live two brothers, one a minister and the other one a hunchback painter of the chapel who lives with his wife. One dreadful and stormy night, a stranger knocks at the door asking for shelter. The stranger talks about all the good things of the earthly life the minister is missing because of his puritanical faith. The minister comes to accept the stranger's viewpoint but it is others who will pay the consequences because the minister will discover the human pleasures thanks to, ehem, his sister- in -law… The tormented minister and his cuckolded brother will die in a strange accident in the chapel and later an infant will be born from the minister's adulterous relationship.0.003503['Yermoliev']['Russia']1917-10-210.087.0[]ReleasedNaNSatan Triumphant0.00.019170.0
45375NaN0.0[]461257en50 years after decriminalisation of homosexuality in the UK, director Daisy Asquith mines the jewels of the BFI archive to take us into the relationships, desires, fears and expressions of gay men and women in the 20th century.0.163015[]['United Kingdom']2017-06-090.075.0['English']ReleasedNaNQueerama0.00.020170.0

Duplicate rows

Most frequently occurring

collection_namebudgetgenresidoriginal_languageoverviewpopularityproduction_companiesproduction_countriesrelease_daterevenueruntimespoken_languagesstatustaglinetitlevote_averagevote_countrelease_yearreturn# duplicates
14NaN0.0['Thriller', 'Mystery']141971fiRecovering from a nail gun shot to the head and 13 months of coma, doctor Pekka Valinta starts to unravel the mystery of his past, still suffering from total amnesia.0.411949['Filmiteollisuus Fine']['Finland']2008-12-260.0108.0['suomi']ReleasedWhich one is the first to return - memory or the murderer?Blackout6.73.020080.03
0Why We Fight0.0['Documentary']159849enThe third film of Frank Capra's 'Why We Fight" propaganda film series, dealing with the Nazi conquest of Western Europe in 1940.0.473322[]['United States of America']1943-01-010.057.0['English']ReleasedNaNWhy We Fight: Divide and Conquer5.01.019430.02
1NaN0.0['Action', 'Drama', 'Romance', 'Adventure']99080enOriginally called White Thunder, American producer Varick Frissell's 1931 film was inspired by his love for the Canadian Arctic Circle. Set in a beautifully black-and-white filmed Newfoundland, it is the story of a rivalry between two seal hunters that plays out on the ice floes during a hunt. Unsatisfied with the first cut, Frissell arranged for the crew to accompany an actual Newfoundland seal hunt on The SS Viking, on which an explosion of dynamite (carried regularly at the time on Arctic ships to combat ice jams) killed many members of the crew, including Frissell. The film was renamed in honor of the dead.0.002362[][]1931-06-210.070.0['English']ReleasedActually produced during the Great Newfoundland Seal Hunt and You see the REAL thingThe Viking0.00.019310.02
2NaN0.0['Action', 'Horror', 'Science Fiction']18440enWhen a comet strikes Earth and kicks up a cloud of toxic dust, hundreds of humans join the ranks of the living dead. But there's bad news for the survivors: The newly minted zombies are hell-bent on eradicating every last person from the planet. For the few human beings who remain, going head to head with the flesh-eating fiends is their only chance for long-term survival. Yet their battle will be dark and cold, with overwhelming odds.1.436085[]['United States of America']2007-01-010.089.0['English']ReleasedNaNDays of Darkness5.05.020070.02
3NaN0.0['Adventure', 'Animation', 'Drama', 'Action', 'Foreign']23305enIn feudal India, a warrior (Khan) who renounces his role as the longtime enforcer to a local lord becomes the prey in a murderous hunt through the Himalayan mountains.1.967992['Filmfour']['France', 'Germany', 'India', 'United Kingdom']2001-09-230.086.0['हिन्दी']ReleasedNaNThe Warrior6.315.020010.02
4NaN0.0['Comedy', 'Drama']11115enAs an ex-gambler teaches a hot-shot college kid some things about playing cards, he finds himself pulled into the world series of poker, where his protégé is his toughest competition.6.880365['Andertainment Group', 'Crescent City Pictures', 'Tag Entertainment']['United States of America']2008-01-290.085.0['English']ReleasedNaNDeal5.222.020080.02
5NaN0.0['Comedy', 'Drama']265189svWhile holidaying in the French Alps, a Swedish family deals with acts of cowardliness as an avalanche breaks out.12.165685['Motlys', 'Coproduction Office', 'Film i Väst']['Norway', 'Sweden', 'France']2014-08-151359497.0118.0['Français', 'Norsk', 'svenska', 'English']ReleasedNaNForce Majeure6.8255.020140.02
6NaN0.0['Comedy']97995enAfter breaking a mirror in his home, superstitious Max tries to avoid situations which could bring bad luck but in doing so, causes himself the worst luck imaginable.0.141558['Max Linder Productions']['United States of America']1921-02-060.062.0['English']ReleasedNaNSeven Years Bad Luck5.64.019210.02
7NaN0.0['Crime', 'Drama', 'Thriller']5511frHitman Jef Costello is a perfectionist who always carefully plans his murders and who never gets caught.9.091288['Fida cinematografica', 'Compagnie Industrielle et Commerciale Cinématographique (CICC)', 'TC Productions', 'Filmel']['France', 'Italy']1967-10-2539481.0105.0['Français']ReleasedThere is no solitude greater than that of the SamuraiLe Samouraï7.9187.019670.02
8NaN0.0['Drama', 'Comedy']168538enIn Zola's Paris, an ingenue arrives at a tony bordello: she's Nana, guileless, but quickly learning to use her erotic innocence to get what she wants. She's an actress for a soft-core filmmaker and soon is the most popular courtesan in Paris, parlaying this into a house, bought for her by a wealthy banker. She tosses him and takes up with her neighbor, a count of impeccable rectitude, and with the count's impressionable son. The count is soon fetching sticks like a dog and mortgaging his lands to satisfy her whims.1.276602['Cannon Group', 'Metro-Goldwyn-Mayer (MGM)'][]1983-06-130.092.0[]ReleasedNaNNana, the True Key of Pleasure4.73.019830.02